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      CoCiter: An Efficient Tool to Infer Gene Function by Assessing the Significance of Literature Co-Citation

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          Abstract

          A routine approach to inferring functions for a gene set is by using function enrichment analysis based on GO, KEGG or other curated terms and pathways. However, such analysis requires the existence of overlapping genes between the query gene set and those annotated by GO/KEGG. Furthermore, GO/KEGG databases only maintain a very restricted vocabulary. Here, we have developed a tool called “CoCiter” based on literature co-citations to address the limitations in conventional function enrichment analysis. Co-citation analysis is widely used in ranking articles and predicting protein-protein interactions (PPIs). Our algorithm can further assess the co-citation significance of a gene set with any other user-defined gene sets, or with free terms. We show that compared with the traditional approaches, CoCiter is a more accurate and flexible function enrichment analysis method. CoCiter is freely available at www.picb.ac.cn/hanlab/cociter/.

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          Gene Ontology: tool for the unification of biology

          Michael Ashburner, Catherine A. Ball, Judith Blake … (2002)
          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Ontological analysis of gene expression data: current tools, limitations, and open problems.

            Purvesh Khatri, Sorin Drăghici (2005)
            Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.
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              A gene network for navigating the literature.

              Robert Hoffmann, Alfonso Valencia (2004)
              Article 0 comments Cited 167 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Vladimir B. Bajic: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2013
                Publication date (Electronic): 23 September 2013
                Volume: 8
                Issue: 9
                Electronic Location Identifier: e74074
                Affiliations
                [1 ]Chinese Academy of Sciences Key Laboratory of Computational Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
                [2 ]Center of Molecular Systems Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
                [3 ]University of Chinese Academy of Sciences, Beijing, China
                King Abdullah University of Science and Technology, Saudi Arabia
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JDJH NQ. Performed the experiments: NQ YH. Analyzed the data: NQ JDJH YH. Wrote the paper: NQ JDJH YH HN CDG.

                Article
                Publisher ID: PONE-D-12-40106
                DOI: 10.1371/journal.pone.0074074
                PMC ID: 3781068
                PubMed ID: 24086311
                SO-VID: c78b2a6b-a815-44b1-8f1d-d6dc4b65af5a
                Copyright statement: Copyright @ 2013
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 18 December 2012
                Date accepted : 30 July 2013
                Page count
                Pages: 8
                Funding
                This project was funded by grants from the National Natural Science Foundation of China (NSFC; grants 31210103916 and 91019019), Chinese Ministry of Science and Technology (grant 2011CB504206), Chinese Academy of Sciences (CAS; grants KSCX2-EW-R-02 and KSCX2-EW-J-15 and YZ201243), Stem Cell Leading Project (XDA01010303) and Shanghai Academic Leader Project (11XD1405700) to JDJH. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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